City administrative region tourism route recommendation algorithm based on naive Bayes data mining

Aiming at the problems existing in the current tourism route planning, this paper constructs a city administrative region tourism route recommendation algorithm based on Naive Bayes data mining. The Naive Bayes classifier model is constructed through the attribute tag data of tourists’ once-visited scenic spots, and then the scenic spots in the target city are classified. The scenic spots are sorted according to the weighted Bayesian probability value of each classification, so as to recommend the scenic spots with the highest probability value for tourists. Based on the scenic spots with the optimal probability values, this paper constructs a tourism route algorithm with the lowest cost. Combined with the different travel modes of tourists, it searches the city tourism routes with the lowest cost on traveling distance, time and fee. At the same time, it provides two tourism decision-making plans according to the actual needs of tourists. Experiment shows that the proposed algorithm can recommend the scenic spots with the highest weighted probability value and satisfy the needs of tourists, and the traveling cost on the searched tourism route is the lowest.